One of the most difficult challenges for risk assessment is evaluation of chemicals that predominately co-occur in mixtures like polycyclic aromatic hydrocarbons (PAHs). We previously developed a classification model in which systems biology data collected from mice short-term after chemical exposure accurately predict tumor outcome. The present study demonstrates translation of this approach into a human in vitro model in which chemical-specific bioactivity profiles from 3D human bronchial epithelial cells (HBEC) classify PAHs by carcinogenic potency. Gene expression profiles were analyzed from HBEC exposed to carcinogenic and non-carcinogenic PAHs and classification accuracies were identified for individual pathway-based gene sets. Posterior probabilities of best performing gene sets were combined via Bayesian integration resulting in a classifier with four gene sets, including aryl hydrocarbon receptor signaling, regulation of epithelial mesenchymal transition, regulation of angiogenesis, and cell cycle G2-M. In addition, transcriptional benchmark dose modeling of benzo[a]pyrene (BAP) showed that the most sensitive gene sets to BAP regulation were largely dissimilar from those that best classified PAH carcinogenicity challenging current assumptions that BAP carcinogenicity (and subsequent mode of action) is reflective of overall PAH carcinogenicity. These results illustrate utility of using systems toxicology approaches to analyze global gene expression towards carcinogenic hazard assessment.

风险评估最困难的挑战之一是评估主要以混合物形式共存的化学品，如多环芳烃 (PAH)。我们之前开发了一种分类模型，其中在化学暴露后短期内从小鼠收集的系统生物学数据可以准确预测肿瘤结果。本研究证明了这种方法可转化为人体体外模型，其中 3D 人支气管上皮细胞 (HBEC) 的化学特异性生物活性谱可根据致癌效力对 PAH 进行分类。对暴露于致癌性和非致癌性 PAH 的 HBEC 的基因表达谱进行了分析，并确定了基于各个途径的基因集的分类准确性。最佳表现基因集的后验概率通过贝叶斯积分进行组合，产生具有四个基因集的分类器，包括芳烃受体信号传导、上皮间质转化的调节、血管生成的调节和细胞周期G2-M。此外，苯并[ a ]芘 (BAP) 的转录基准剂量模型表明，对 BAP 调节最敏感的基因集与那些最能分类 PAH 致癌性的基因集在很大程度上不同，这挑战了当前 BAP 致癌性（以及随后的作用模式）是反映了总体 PAH 的致癌性。这些结果说明了使用系统毒理学方法分析全局基因表达以进行致癌危害评估的效用。